I'm migrating data from one system to another and will be receiving a CSV file with the data to import. The file could contain up to a million records to import. I need to get each line in the file, validate it and put the data into the relevant tables. For example, the CSV would be like:
Mr,Bob,Smith,1 high street,London,ec1,012345789,work(this needs to be looked up in another table to get the ID)
There's a lot more data than this example in the real files.
So, the SQL would be something like this:
Declare #UserID
Insert into User
Values ('Mr', 'Bob', 'Smith', 0123456789)
Set #UserID = ##Identity
Insert into Address
Values ('1 high street', 'London', 'ec1', select ID from AddressType where AddressTypeName = 'work')
I was thinking of iterating over each row and call an SP with the parameters from the file which will contain the SQL above. Would this be the best way of tackling this? It's not time critical as this will just be run once when updating a site.
I'm using C# and SQL Server 2008 R2.
What about you load it into a temporary table (note that this may be logically temporary - not necessarily technically) as staging, then process it from there. This is standard ETL behavior (and a million is tiny for ETL), you first stage the data, then clean it, then put it to the final place.
When performing tasks of this nature, you do not think in terms of rotating through each record individually as that will be a huge performence problem. In this case you bulk insert the records to a staging table or use the wizard to import to a staging table (look out for teh deafult 50 characters espcially in the address field).Then you write set-based code to do any clean up you need (removing bad telephone numbers or zip code or email addresses or states or records missing data in fields that are required in your database or transforing data using lookup tables (suppose you have table with certain required values, those are likely not the same values that you wil find in this file, you need to convert them. We use doctor specialties a lot. So our system might store them as GP but the file might give us a value of General Practioner. You need to look at all teh non-matching values for the field and then determine if you can map them to existing values, if you need to throw the record out or if you need to add more values to your lookup table. Once you have gotten rid of records you don't want and cleaned up those you can in your staging table then you import to the prod tables. Inserts should be written using the SELECT version of INSERT not with the VALUES clause when you are writing more than one or two records.
Related
Evening all,
Background: I have many xml data files which I need to import in to an SQL database via my C# WPF based Windows Application. Whilst some data files are a simple and straight forward INSERT, many require validation and verification checks, require GUIDs from existing records (from within the db) before the INSERT takes place in other to maintain certain relationships.
So I've broken the process in to three stages:-
Validate records. Many checks exist such as e.g. 50,000 accounts in xml file must have a matching reference with an associated record already in the database. If there is no corresponding account, abandon the entire import process; Another would be, only the 'Current' record can be updated, so again, if it points to a 'historic' record then it needs to crash and burn.
UPDATE the associated database records e.g Set from 'Current' to 'Historic' as record is to be superseded by what's to come next...
INSERT the records across multiple tables. e.g. 50,000 accounts to be inserted.
So, in summary, I need to validate records before any changes can be made to the database with a few validation checks. At which point I will change various tables for existing records before inserting the 'latest' records.
My first attempt to resolve this situation was to load the xml file in to XmlDocument or XDocument instance, iterate over every account in the xml file and perform an sql command for every account. Verify it exists, verify its a current account, change the record before inserting it. Rinse and repeat for thousands of records - Not ideal to say the least.
So my second attempt is to load the xml file in to a data table. Export the corresponding accounts from the database in to another data table and perform a nested loop validation e.g. does DT1.AccountID exist in DT2.AccountID, move DT2.GUID to DT1.GUID etc etc. I appreciate this could also be a slow process. That said, I do have the luxury then of performing both the UPDATE and INSERT stored procedures with a table value parameter (TVP) and making use of the data table information.
I appreciate many will suggest letting the SQL do all of the work but i'm lacking in that skillset unfortunately (happy to learn if thats the general consensus) but I would much rather do the work in C# code if at all possible.
Any views on this are greatly appreciated. Many of the questions I've found are around bulk INSERT, not so much about validating existing records, following by updating records followed by inserting records. I suppose my question is around the first part, the validation. Does extracting the data from the db in to a data table to work on seem wrong, old fashioned, pointless?
I'm sure i've missed out some vital piece of information so apologies if unclear.
Cheers
I have about 1.5 million files I need to insert records for in the database.
Each record is inserted with a key that includes the name of the file.
The catch: The files are not uniquely identified currently.
So, what we'd like to do is, for each file:
Insert a record. One of the fields in the record should include an amazon S3 key which should include the ID of the newly inserted record.
Rename the file to include the ID so that it matches the format of the key.
The best thing I can think to do is:
Run an individual insert command that returns the ID of the added row.
Add that back as a property to the individual business object I'm looping through.
Generate an update statement that updates the S3 key to include the ID
Output the file, concatenate the ID into the end the file name.
As I can tell, that looks to be :
1.5 million insert statements
individual SqlCommand executions and read because we need the ID back),
1.5 million times setting a property on an object.
1.5 million update statements generated and executed
Perhaps could make this a one giant concatenated update statement to do them all at once; not sure if that helps
1.5 million file copies.
I can't get around the actual file part, but for the rest, is there a better strategy I'm not seeing?
If you make the client application generate the IDs you can use a straight-forward SqlBulkCopy to insert all rows at once. It will be done in seconds.
If you want to keep the IDENTITY property of the column, you can run a DBCC CHECKIDENT(RESEED) to advance the identity counter by 1.5m to give you a guaranteed gap that you can insert into. If the number of rows is not statically known you can perform the inserting in smaller chunks of maybe 100k until you are done.
You will cut the number of SQL statements in half up by not relying on the database to generate your ID for each row. Do everything locally (including the assignment of an ID) and then do a single batch of inserts at the end, with identity_insert on.
This will cause SQL Server to use your ID's for this batch of records.
If this is still too slow (and 1.5 million inserts might be), the next step would be to output your data to a text file (XML, comma delimited, or whatever) and then do a bulk import operation on the file.
That's as fast as you will be able to make it, I think.
I have a VERY large (50 million+ records) dataset that I am importing from an old Interbase database into a new SQL Server database.
My current approach is:
acquire csv files from the Interbase database (done, used a program called "FBExport" I found somewhere online)
The schema of the old database doesn't match the new one (not under my control), so now I need to mass edit certain fields in order for them to work in the new database. This is the area I need help with
after editing to the correct schema, I am using SqlBulkCopy to copy the newly edited data set into the SQL Server database.
Part 3 works very quickly, diagnostics shows that importing 10,000 records at once is done almost instantly.
My current (slow) approach to part 2 is I just read the csv file line by line, and lookup the relevant information (ex. the csv file has an ID that is XXX########, whereas the new database has a separate column for each XXX and ########. ex2. the csv file references a model via a string, but the new database references via an ID in the model table) and then insert a new row into my local table, and then SqlBulkCopy after my local table gets large.
My question is: What would be the "best" approach (perfomance wise) for this data-editing step? I figure there is very likely a linq-type approach to this, would that perform better, and how would I go about doing that if it would?
If step #3’s importing is very quick, I would be tempted to create a temporary database whose schema exactly matches the old database and import the records into it. Then I’d look at adding additional columns to the temporary table where you need to split the XXX######## into XXX and ########. You could then use SQL to split the source column into the two separate ones. You could likewise use SQL to do whatever ID based lookups and updates you need to ensure the record relationships continue to be correct.
Once the data has been massaged into a format which is acceptable, you can insert the records into the final tables using IDENTITY_INSERT ON, excluding all legacy columns/information.
In my mind, the primary advantage of doing it within the temporary SQL DB is that at any time you can write queries to ensure that record relationships using the old key(s) are still correctly related to records using the new database’s auto generated keys.
This is of coursed based on me being more comfortable doing data transformations/validation in SQL than in C#.
I'm currently working on a project where we have a large data warehouse which imports several GB of data on a daily basis from a number of different sources. We have a lot of files with different formats and structures all being imported into a couple of base tables which we then transpose/pivot through stored procs. This part works fine. The initial import however, is awfully slow.
We can't use SSIS File Connection Managers as the columns can be totally different from file to file so we have a custom object model in C# which transposes rows and columns of data into two base tables; one for column names, and another for the actual data in each cell, which is related to a record in the attribute table.
Example - Data Files:
Example - DB tables:
The SQL insert is performed currently by looping through all the data rows and appending the values to a SQL string. This constructs a large dynamic string which is then executed at the end via SqlCommand.
The problem is, even running in a 1MB file takes about a minute, so when it comes to large files (200MB etc) it takes hours to process a single file. I'm looking for suggestions as to other ways to approach the insert that will improve performance and speed up the process.
There are a few things I can do with the structure of the loop to cut down on the string size and number of SQL commands present in the string but ideally I'm looking for a cleaner, more robust approach. Apologies if I haven't explained myself well, I'll try and provide more detail if required.
Any ideas on how to speed up this process?
The dynamic string is going to be SLOW. Each SQLCommand is a separate call to the database. You are much better off streaming the output as a bulk insertion operation.
I understand that all your files are different formats, so you are having to parse and unpivot in code to get it into your EAV database form.
However, because the output is in a consistent schema you would be better off either using separate connection managers and the built-in unpivot operator, or in a script task adding multiple rows to the data flow in the common output (just like you are currently doing in building your SQL INSERT...INSERT...INSERT for each input row) and then letting it all stream into a destination.
i.e. Read your data and in the script source, assign the FileID, RowId, AttributeName and Value to multiple rows (so this is doing the unpivot in code, but instead of generating a varying number of inserts, you are just inserting a varying number of rows into the dataflow based on the input row).
Then pass that through a lookup to get from AttributeName to AttributeID (erroring the rows with invalid attributes).
Stream straight into an OLEDB destination, and it should be a lot quicker.
One thought - are you repeatedly going back to the database to find the appropriate attribute value? If so, switching the repeated queries to a query against a recordset that you keep at the clientside will speed things up enormously.
This is something I have done before - 4 reference tables involved. Creating a local recordset and filtering that as appropriate caused a speed up of a process from 2.5 hours to about 3 minutes.
Why not store whatever reference tables are needed within each database and perform all lookups on the database end? Or it may even be better to pass a table type into each database where keys are needed, store all reference data in one central database and then perform your lookups there.
I'm building an application to import data into a sql server 2008 Express db.
This database is being used by an application that is currently in production.
The data that needs to be imported comes from various sources, mostly excel sheets and xml files.
The database has the following tables:
tools
powertools
strikingtools
owners
Each row, or xml tag in the source files has information about 1 tool:
name, tooltype, weight, wattage, owner, material, etc...
Each of these rows has the name of the tool's owner this name has to be inserted into the owners table but only if the name isn't already in there.
For each of these rows a new row needs to be inserted in the tools table.
The tools table has a field owner_id with a foreign key to the owners table where the primary key of the corresponding row in the owners table needs to be set
Depending on the tooltype a new row must be created in either the powertools table or the strikingtools table. These 2 tables also have a tool_id field with a foreign key to the tools table that must be filled in.
The tools table has a tool_owner_id field with a foreign key to the owners table that must be filled in.
If any of the rows in the importfile fails to import for some reason, the entire import needs to be rolled back
Currently I'm using a dataset to do this but for some large files (over 200.000 tools) this requires quite a lot of memory. Can anybody think of a better aproach for this?
There are two main issues to be solved:
Parsing the a large XML document efficiently.
Adding a large amount of records to the database.
XML Parsing
Although the DataSet approach works, the whole XML document is loaded into memory. To improve the efficiency of working with large XML documents you might want look at the XmlReader class. The API is slightly more difficult to use than what DataSet provides. But you will get the benefit of not loading the whole DOM into memory at once.
Inserting records to the DB
To satisfy your Atomicity requirement you can use a single database transaction but the large number of records you are dealing with for a single transaction is not ideal. You will most likely incur issues like:
Database having to deal with a large number of locks
Database locks that might escalate from row locks to page locks and even table locks.
Concurrent use of the database will be severely affect during the import.
I would recommend the following instead of a single DB transaction:
See if it possible to create smaller transaction batches. Maybe 100 records at a time. Perhaps it is possible to logically load sections of the XML file together, where it would be acceptable load a subset of the data as a unit into the system.
Validate as much of your data upfront. E.g. Check that required fields are filled or that FK's are correct.
Make the upload repeatable. Skip over existing data.
Provide a manual undo strategy. I know this is easier said than done, but might even be required as an additional business rule. For example the upload was successful but someone realises a couple of hours later that the wrong file was uploaded.
It might be useful to upload your data to a initial staging area in your DB to perform validations and to mark which records have been processed.
Use SSIS, and create and ETL package.
Use Transactions for the roll back feature, and stored procedure that handle creating/checking the foreign keys.